1. Background Field
Embodiments of the subject matter described herein are related generally to position and tracking, and more particularly to vision based tracking of mobile devices.
2. Relevant Background
Highly accurate 6-degree-of-freedom (DOF) self-localization with respect to the user's environment is an inevitable necessity for correct and visually pleasing results in Augmented Reality (AR). An efficient way to perform self-localization is to use sparse 3D point cloud reconstructions of the environment and to perform feature matching between the camera live image and the reconstruction. From the feature matches, the position and orientation, i.e., the pose, can be estimated. A challenge that is faced in mobile AR, e.g., AR performed on mobile devices such as cellular telephones or smart phones, is that the pose estimate is often generated in wide-area environments, for example, outdoors. Due to the interactive nature of AR applications, localization time has a direct impact on the user experience of an AR application, because it determines how long the user must wait before interaction with the application may start. Thus, it is desirable to localize a mobile device quickly, e.g., within a few seconds, with the limited processing power found in mobile devices, while maintaining the necessary accuracy in the pose (position and orientation) for the desired application, e.g., sub-meter accuracy for position and less than 5° angular error for orientation.
In the Computer Vision (CV) field, the localization problem has been solved mainly on a coarse scale using computationally demanding algorithms. Moreover, the localization task typically is solved with accuracies of up to several meters. Additionally, typical localization solutions determine a position only with two-degrees of freedom (2DOF) or three degrees of freedom (3DOF), rather than a full six-degrees of freedom (6DOF) pose. Therefore, conventional localization approaches are not directly suitable for mobile AR applications or other similarly demanding applications.